Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNeural Network-Based Generation of Sport Summaries: A Preliminary Study
الجيل القائم على الشبكة العصبية من الملخصات الرياضية: دراسة أولية
317
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تقدم هذه الورقة طريقة تلخيص عالمية لتعليقات الرياضة الحية التي لدينا ملخص مكتوب بشري متاح.تستند هذه الطريقة إلى ملخص مولد عصبي.يتم تقييد كمية البيانات المتاحة للتدريب مقارنة بالشريعة المستخدمة عادة من قبل الملخصات العصبية.نقترح لمساعدة الملخص على التعلم من كمية محدودة من البيانات عن طريق الحد من انتروبيا من نصوص الإدخال.يتم تنفيذ هذه الخطوة من خلال تصنيف إلى فئات مستمدة من تحليل مفصل للملخصات التي كتبها الإنسان.نظهر أن الترشيح يساعد نظام التلخيص للتغلب على نقص الموارد.ومع ذلك، ظهرت عدة نقاط تحسين من هذه الدراسة الأولية، والتي نناقشها وتخطط لتنفيذها في العمل في المستقبل.
This paper presents a global summarization method for live sport commentaries for which we have a human-written summary available. This method is based on a neural generative summarizer. The amount of data available for training is limited compared to corpora commonly used by neural summarizers. We propose to help the summarizer to learn from a limited amount of data by limiting the entropy of the input texts. This step is performed by a classification into categories derived by a detailed analysis of the human-written summaries. We show that the filtering helps the summarization system to overcome the lack of resources. However, several improving points have emerged from this preliminary study, that we discuss and plan to implement in future work.
References used
https://aclanthology.org/
rate research
Read More
Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summar
izers. In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model. Experiments across models and datasets reveal that the summarizer learns both, dependency- and constituency-style discourse information, which is typically encoded in a single head, covering long- and short-distance discourse dependencies. Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain.
Reference-based automatic evaluation metrics are notoriously limited for NLG due to their inability to fully capture the range of possible outputs. We examine a referenceless alternative: evaluating the adequacy of English sentences generated from Ab
stract Meaning Representation (AMR) graphs by parsing into AMR and comparing the parse directly to the input. We find that the errors introduced by automatic AMR parsing substantially limit the effectiveness of this approach, but a manual editing study indicates that as parsing improves, parsing-based evaluation has the potential to outperform most reference-based metrics.
Modern approaches to Constituency Parsing are mono-lingual supervised approaches which require large amount of labelled data to be trained on, thus limiting their utility to only a handful of high-resource languages. To address this issue of data-spa
rsity for low-resource languages we propose Universal Recurrent Neural Network Grammars (UniRNNG) which is a multi-lingual variant of the popular Recurrent Neural Network Grammars (RNNG) model for constituency parsing. UniRNNG involves Cross-lingual Transfer Learning for Constituency Parsing task. The architecture of UniRNNG is inspired by Principle and Parameter theory proposed by Noam Chomsky. UniRNNG utilises the linguistic typology knowledge available as feature-values within WALS database, to generalize over multiple languages. Once trained on sufficiently diverse polyglot corpus UniRNNG can be applied to any natural language thus making it Language-agnostic constituency parser. Experiments reveal that our proposed UniRNNG outperform state-of-the-art baseline approaches for most of the target languages, for which these are tested.
In simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in producing target tokens remains an extremely challenging
problem. We propose a novel supervised learning approach for training an agent that can detect the minimum number of reads required for generating each target token by comparing simultaneous translations against full-sentence translations during training to generate oracle action sequences. These oracle sequences can then be used to train a supervised model for action generation at inference time. Our approach provides an alternative to current heuristic methods in simultaneous translation by introducing a new training objective, which is easier to train than previous attempts at training the agent using reinforcement learning techniques for this task. Our experimental results show that our novel training method for action generation produces much higher quality translations while minimizing the average lag in simultaneous translation.
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells. In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks. In contrast to the previous CDG
tasks which focus on generating documentation for single code snippets, in a computational notebook, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. We proposed a new model (HAConvGNN) that uses a hierarchical attention mechanism to consider the relevant code cells and the relevant code tokens information when generating the documentation. Tested on a new corpus constructed from well-documented Kaggle notebooks, we show that our model outperforms other baseline models.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
